Perhaps the biggest obstacle to the adoption lies in the industry's unfamiliarity with the tools and methods that QbD uses
to achieve comprehensive product and process knowledge and control: high-end statistics (i.e., multivariate analysis), modeling
tools, design of experiments, etc. These tools have been used for decades in the chemical, engineering, and high-technology
industries, and taught in graduate programs that lead to those industries. Graduate programs in life sciences do not universally
teach these skills and they are not fully integrated and applied in the life sciences industries. As a result, many biotech
industry scientists are simply unaware of the power, applicability, and track record of these powerful techniques.
That is about to change, as biotech companies seek innovative ways—including QbD—to respond to the multiple pressures and
trends sweeping the industry. It is those QbD tools that are used to determine the all-important Design Space, defined in
ICH Q8 as "the multi-dimensional combination and interaction of input variables (e.g., material attributes) and process parameters
that have been demonstrated to provide assurance of quality."2 How those tools work to describe that linkage between the process inputs and the critical quality attributes (CQAs) is the
subject of the next installment in this series.
Conrad J. Heilman, Jr., PhD, is senior vice president at Tunnell Consulting, King of Prussia, PA, 610.337.0820, firstname.lastname@example.org
1. Rathore AS, Johnson R, Yu O, Kirdar AO, Alaqappan A, Ahuja S, Ram K. Applications of multivariate data analysis in biotech
processing. BioPharm Int. 2007 Oct;20(10).
2. International Conference on Harmonization. Q8, Pharmaceutical development. Geneva, Switzerland; 2005.